


import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase     		=							logging.get_logger(__name__)
def 							UpperCamelCase_(					_A :Tuple					,  _A :str					,  _A :int	)->		Optional[Any]:
			UpperCamelCase__       =							os.path.abspath(_A	)
			logger.info(F'''Converting TensorFlow checkpoint from {tf_path}'''	)
			# Load weights from TF model
			UpperCamelCase__       =							tf.train.list_variables(_A	)
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							[]
			for full_name, shape in init_vars:
						# logger.info(f"Loading TF weight {name} with shape {shape}")
						UpperCamelCase__       =							full_name.split("/"	)
						if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
									logger.info(F'''Skipping non-model layer {full_name}'''	)
									continue
						if "optimizer" in full_name:
									logger.info(F'''Skipping optimization layer {full_name}'''	)
									continue
						if name[0] == "model":
									# ignore initial 'model'
									UpperCamelCase__       =							name[1:]
						# figure out how many levels deep the name is
						UpperCamelCase__       =							0
						for _name in name:
									if _name.startswith("layer_with_weights"	):
												depth += 1
									else:
												break
						layer_depth.append(_A	)
						# read data
						UpperCamelCase__       =							tf.train.load_variable(_A					,  _A	)
						names.append("/".join(_A	)	)
						arrays.append(_A	)
			logger.info(F'''Read a total of {len(_A	):,} layers'''	)

			# Sanity check
			if len(set(_A	)	) != 1:
						raise ValueError(F'''Found layer names with different depths (layer depth {list(set(_A	)	)})'''	)
			UpperCamelCase__       =							list(set(_A	)	)[0]
			if layer_depth != 1:
						raise ValueError(
						    "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
						    " heads."	)

			# convert layers
			logger.info("Converting weights..."	)
			for full_name, array in zip(_A					,  _A	):
						UpperCamelCase__       =							full_name.split("/"	)
						UpperCamelCase__       =							model
						UpperCamelCase__       =							[]
						for i, m_name in enumerate(_A	):
									if m_name == ".ATTRIBUTES":
												# variable names end with .ATTRIBUTES/VARIABLE_VALUE
												break
									if m_name.startswith("layer_with_weights"	):
												UpperCamelCase__       =							int(m_name.split("-"	)[-1]	)
												if layer_num <= 2:
															# embedding layers
															# layer_num 0: word_embeddings
															# layer_num 1: position_embeddings
															# layer_num 2: token_type_embeddings
															continue
												elif layer_num == 3:
															# embedding LayerNorm
															trace.extend(["embeddings", "LayerNorm"]	)
															UpperCamelCase__       =							getattr(_A					,  "embeddings"	)
															UpperCamelCase__       =							getattr(_A					,  "LayerNorm"	)
												elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
															# encoder layers
															trace.extend(["encoder", "layer", str(layer_num - 4	)]	)
															UpperCamelCase__       =							getattr(_A					,  "encoder"	)
															UpperCamelCase__       =							getattr(_A					,  "layer"	)
															UpperCamelCase__       =							pointer[layer_num - 4]
												elif layer_num == config.num_hidden_layers + 4:
															# pooler layer
															trace.extend(["pooler", "dense"]	)
															UpperCamelCase__       =							getattr(_A					,  "pooler"	)
															UpperCamelCase__       =							getattr(_A					,  "dense"	)
									elif m_name == "embeddings":
												trace.append("embeddings"	)
												UpperCamelCase__       =							getattr(_A					,  "embeddings"	)
												if layer_num == 0:
															trace.append("word_embeddings"	)
															UpperCamelCase__       =							getattr(_A					,  "word_embeddings"	)
												elif layer_num == 1:
															trace.append("position_embeddings"	)
															UpperCamelCase__       =							getattr(_A					,  "position_embeddings"	)
												elif layer_num == 2:
															trace.append("token_type_embeddings"	)
															UpperCamelCase__       =							getattr(_A					,  "token_type_embeddings"	)
												else:
															raise ValueError(F'''Unknown embedding layer with name {full_name}'''	)
												trace.append("weight"	)
												UpperCamelCase__       =							getattr(_A					,  "weight"	)
									elif m_name == "_attention_layer":
												# self-attention layer
												trace.extend(["attention", "self"]	)
												UpperCamelCase__       =							getattr(_A					,  "attention"	)
												UpperCamelCase__       =							getattr(_A					,  "self"	)
									elif m_name == "_attention_layer_norm":
												# output attention norm
												trace.extend(["attention", "output", "LayerNorm"]	)
												UpperCamelCase__       =							getattr(_A					,  "attention"	)
												UpperCamelCase__       =							getattr(_A					,  "output"	)
												UpperCamelCase__       =							getattr(_A					,  "LayerNorm"	)
									elif m_name == "_attention_output_dense":
												# output attention dense
												trace.extend(["attention", "output", "dense"]	)
												UpperCamelCase__       =							getattr(_A					,  "attention"	)
												UpperCamelCase__       =							getattr(_A					,  "output"	)
												UpperCamelCase__       =							getattr(_A					,  "dense"	)
									elif m_name == "_output_dense":
												# output dense
												trace.extend(["output", "dense"]	)
												UpperCamelCase__       =							getattr(_A					,  "output"	)
												UpperCamelCase__       =							getattr(_A					,  "dense"	)
									elif m_name == "_output_layer_norm":
												# output dense
												trace.extend(["output", "LayerNorm"]	)
												UpperCamelCase__       =							getattr(_A					,  "output"	)
												UpperCamelCase__       =							getattr(_A					,  "LayerNorm"	)
									elif m_name == "_key_dense":
												# attention key
												trace.append("key"	)
												UpperCamelCase__       =							getattr(_A					,  "key"	)
									elif m_name == "_query_dense":
												# attention query
												trace.append("query"	)
												UpperCamelCase__       =							getattr(_A					,  "query"	)
									elif m_name == "_value_dense":
												# attention value
												trace.append("value"	)
												UpperCamelCase__       =							getattr(_A					,  "value"	)
									elif m_name == "_intermediate_dense":
												# attention intermediate dense
												trace.extend(["intermediate", "dense"]	)
												UpperCamelCase__       =							getattr(_A					,  "intermediate"	)
												UpperCamelCase__       =							getattr(_A					,  "dense"	)
									elif m_name == "_output_layer_norm":
												# output layer norm
												trace.append("output"	)
												UpperCamelCase__       =							getattr(_A					,  "output"	)
									# weights & biases
									elif m_name in ["bias", "beta"]:
												trace.append("bias"	)
												UpperCamelCase__       =							getattr(_A					,  "bias"	)
									elif m_name in ["kernel", "gamma"]:
												trace.append("weight"	)
												UpperCamelCase__       =							getattr(_A					,  "weight"	)
									else:
												logger.warning(F'''Ignored {m_name}'''	)
        # for certain layers reshape is necessary
						UpperCamelCase__       =							".".join(_A	)
						if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"					,  _A	) or re.match(
						    r"(\S+)\.attention\.output\.dense\.weight"					,  _A	):
									UpperCamelCase__       =							array.reshape(pointer.data.shape	)
						if "kernel" in full_name:
									UpperCamelCase__       =							array.transpose()
						if pointer.shape == array.shape:
									UpperCamelCase__       =							torch.from_numpy(_A	)
						else:
									raise ValueError(
									    F'''Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'''
									    F''' {array.shape}'''	)
						logger.info(F'''Successfully set variable {full_name} to PyTorch layer {trace}'''	)
			return model
def 							UpperCamelCase_(					_A :Union[str, Any]					,  _A :Dict					,  _A :Dict	)->		Dict:
			# Instantiate model
			logger.info(F'''Loading model based on config from {config_path}...'''	)
			UpperCamelCase__       =							BertConfig.from_json_file(_A	)
			UpperCamelCase__       =							BertModel(_A	)

			# Load weights from checkpoint
			logger.info(F'''Loading weights from checkpoint {tf_checkpoint_path}...'''	)
			load_tfa_weights_in_bert(_A					,  _A					,  _A	)

			# Save pytorch-model
			logger.info(F'''Saving PyTorch model to {pytorch_dump_path}...'''	)
			torch.save(model.state_dict()					,  _A	)


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()
						parser.add_argument(
						    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x checkpoint path.'
						)
						parser.add_argument(
						    '--bert_config_file',
						    type=str,
						    required=True,
						    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
						)
						parser.add_argument(
						    '--pytorch_dump_path',
						    type=str,
						    required=True,
						    help='Path to the output PyTorch model (must include filename).',
						)
						__UpperCamelCase     		=							parser.parse_args()
						convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






from __future__ import annotations

import math
import random
from typing import Any







class 							lowerCamelCase__						:






			"""simple docstring"""






			def __init__(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							[]
						UpperCamelCase__       =							0
						UpperCamelCase__       =							0




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.head == self.tail




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						self.data.append(snake_case  )
						UpperCamelCase__       =							self.tail + 1




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.data[self.head]
						UpperCamelCase__       =							self.head + 1
						return ret




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.tail - self.head







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						print(self.data  )
						print("**************"  )
						print(self.data[self.head : self.tail]  )







class 							lowerCamelCase__						:






			"""simple docstring"""






			def __init__(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							data
						UpperCamelCase__       =							None
						UpperCamelCase__       =							None
						UpperCamelCase__       =							1




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.data




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.left




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.right




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.height




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							data




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							node




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							node







			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							height



def 							UpperCamelCase_(					_A :MyNode | None	)->		int:
			if node is None:
						return 0
			return node.get_height()
def 							UpperCamelCase_(					_A :int					,  _A :int	)->		int:
			if a > b:
						return a
			return b
def 							UpperCamelCase_(					_A :MyNode	)->		MyNode:
			print("left rotation node:"					,  node.get_data()	)
			UpperCamelCase__       =							node.get_left()
			assert ret is not None
			node.set_left(ret.get_right()	)
			ret.set_right(_A	)
			UpperCamelCase__       =							my_max(get_height(node.get_right()	)					,  get_height(node.get_left()	)	) + 1
			node.set_height(_A	)
			UpperCamelCase__       =							my_max(get_height(ret.get_right()	)					,  get_height(ret.get_left()	)	) + 1
			ret.set_height(_A	)
			return ret
def 							UpperCamelCase_(					_A :MyNode	)->		MyNode:
			print("right rotation node:"					,  node.get_data()	)
			UpperCamelCase__       =							node.get_right()
			assert ret is not None
			node.set_right(ret.get_left()	)
			ret.set_left(_A	)
			UpperCamelCase__       =							my_max(get_height(node.get_right()	)					,  get_height(node.get_left()	)	) + 1
			node.set_height(_A	)
			UpperCamelCase__       =							my_max(get_height(ret.get_right()	)					,  get_height(ret.get_left()	)	) + 1
			ret.set_height(_A	)
			return ret
def 							UpperCamelCase_(					_A :MyNode	)->		MyNode:
			UpperCamelCase__       =							node.get_left()
			assert left_child is not None
			node.set_left(left_rotation(_A	)	)
			return right_rotation(_A	)
def 							UpperCamelCase_(					_A :MyNode	)->		MyNode:
			UpperCamelCase__       =							node.get_right()
			assert right_child is not None
			node.set_right(right_rotation(_A	)	)
			return left_rotation(_A	)
def 							UpperCamelCase_(					_A :MyNode | None					,  _A :Any	)->		MyNode | None:
			if node is None:
						return MyNode(_A	)
			if data < node.get_data():
						node.set_left(insert_node(node.get_left()					,  _A	)	)
						if (
						    get_height(node.get_left()	) - get_height(node.get_right()	) == 2
						):  # an unbalance detected
									UpperCamelCase__       =							node.get_left()
									assert left_child is not None
									if (
									    data < left_child.get_data()
									):  # new node is the left child of the left child
												UpperCamelCase__       =							right_rotation(_A	)
									else:
												UpperCamelCase__       =							lr_rotation(_A	)
			else:
						node.set_right(insert_node(node.get_right()					,  _A	)	)
						if get_height(node.get_right()	) - get_height(node.get_left()	) == 2:
									UpperCamelCase__       =							node.get_right()
									assert right_child is not None
									if data < right_child.get_data():
												UpperCamelCase__       =							rl_rotation(_A	)
									else:
												UpperCamelCase__       =							left_rotation(_A	)
			UpperCamelCase__       =							my_max(get_height(node.get_right()	)					,  get_height(node.get_left()	)	) + 1
			node.set_height(_A	)
			return node
def 							UpperCamelCase_(					_A :MyNode	)->		Any:
			while True:
						UpperCamelCase__       =							root.get_right()
						if right_child is None:
									break
						UpperCamelCase__       =							right_child
			return root.get_data()
def 							UpperCamelCase_(					_A :MyNode	)->		Any:
			while True:
						UpperCamelCase__       =							root.get_left()
						if left_child is None:
									break
						UpperCamelCase__       =							left_child
			return root.get_data()
def 							UpperCamelCase_(					_A :MyNode					,  _A :Any	)->		MyNode | None:
			UpperCamelCase__       =							root.get_left()
			UpperCamelCase__       =							root.get_right()
			if root.get_data() == data:
						if left_child is not None and right_child is not None:
									UpperCamelCase__       =							get_left_most(_A	)
									root.set_data(_A	)
									root.set_right(del_node(_A					,  _A	)	)
						elif left_child is not None:
									UpperCamelCase__       =							left_child
						elif right_child is not None:
									UpperCamelCase__       =							right_child
						else:
									return None
			elif root.get_data() > data:
						if left_child is None:
									print("No such data"	)
									return root
						else:
									root.set_left(del_node(_A					,  _A	)	)
			else:  # root.get_data() < data
						if right_child is None:
									return root
						else:
									root.set_right(del_node(_A					,  _A	)	)

			if get_height(_A	) - get_height(_A	) == 2:
						assert right_child is not None
						if get_height(right_child.get_right()	) > get_height(right_child.get_left()	):
									UpperCamelCase__       =							left_rotation(_A	)
						else:
									UpperCamelCase__       =							rl_rotation(_A	)
			elif get_height(_A	) - get_height(_A	) == -2:
						assert left_child is not None
						if get_height(left_child.get_left()	) > get_height(left_child.get_right()	):
									UpperCamelCase__       =							right_rotation(_A	)
						else:
									UpperCamelCase__       =							lr_rotation(_A	)
			UpperCamelCase__       =							my_max(get_height(root.get_right()	)					,  get_height(root.get_left()	)	) + 1
			root.set_height(_A	)
			return root







class 							lowerCamelCase__						:






			"""simple docstring"""






			def __init__(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							None




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return get_height(self.root  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						print("insert:" + str(snake_case  )  )
						UpperCamelCase__       =							insert_node(self.root   ,    snake_case  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						print("delete:" + str(snake_case  )  )
						if self.root is None:
									print("Tree is empty!"  )
									return
						UpperCamelCase__       =							del_node(self.root   ,    snake_case  )







			def __str__(				self   ,    ):  # a level traversale, gives a more intuitive look on the tree

						'''simple docstring'''


						UpperCamelCase__       =							""
						UpperCamelCase__       =							MyQueue()
						q.push(self.root  )
						UpperCamelCase__       =							self.get_height()
						if layer == 0:
									return output
						UpperCamelCase__       =							0
						while not q.is_empty():
									UpperCamelCase__       =							q.pop()
									UpperCamelCase__       =							" " * int(math.pow(2   ,    layer - 1  )  )
									output += space
									if node is None:
												output += "*"
												q.push(snake_case  )
												q.push(snake_case  )
									else:
												output += str(node.get_data()  )
												q.push(node.get_left()  )
												q.push(node.get_right()  )
									output += space
									UpperCamelCase__       =							cnt + 1
									for i in range(100  ):
												if cnt == math.pow(2   ,    snake_case  ) - 1:
															UpperCamelCase__       =							layer - 1
															if layer == 0:
																		output += "\n*************************************"
																		return output
															output += "\n"
															break
						output += "\n*************************************"
						return output



def 							UpperCamelCase_(					)->		None:
			import doctest

			doctest.testmod()


if __name__ == "__main__":
						_test()
						__UpperCamelCase     		=							AVLtree()
						__UpperCamelCase     		=							list(range(1_0))
						random.shuffle(lst)
						for i in lst:
												t.insert(i)
												print(str(t))
						random.shuffle(lst)
						for i in lst:
												t.del_node(i)
												print(str(t))


